Evidence map›Paper›PMID 42208150›Full record

ArticleEBioMedicine2026

Translating transcriptomics analysis into diagnostic workflows: clinical variant identification and interpretation in hypothesis-driven and hypothesis-free approaches.

Chingyiu Pang, Martin Man-Chun Chui, Wenshu Tang, Anna Ka-Yee Kwong, Hiu Yu Cherie Leung, Sze-Shing Fan, Alice Wing-Sze Kwok, Godfrey Chi-Fung Chan, Ho Ming Luk, Rosanna Ming-Sum Wong and 9 more

Abstract read
In one paragraph

Article in EBioMedicine, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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0cells of the map it votes in
0citing papers in PubMed
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1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

19 authors.

Chingyiu PangDepartment of Paediatrics and Adolescent Medicine, School of Clinical Medicine, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong SAR, China.
Martin Man-Chun ChuiDepartment of Paediatrics and Adolescent Medicine, School of Clinical Medicine, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong SAR, China.
Wenshu TangHong Kong Genome Institute, Hong Kong SAR, China.
Anna Ka-Yee KwongDepartment of Paediatrics and Adolescent Medicine, School of Clinical Medicine, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong SAR, China.
Hiu Yu Cherie LeungDepartment of Paediatrics and Adolescent Medicine, School of Clinical Medicine, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong SAR, China.
Sze-Shing FanDepartment of Paediatrics and Adolescent Medicine, School of Clinical Medicine, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong SAR, China.
Alice Wing-Sze KwokDepartment of Paediatrics and Adolescent Medicine, School of Clinical Medicine, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong SAR, China.
Godfrey Chi-Fung ChanDepartment of Paediatrics and Adolescent Medicine, School of Clinical Medicine, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong SAR, China; Department of Paediatrics and Adolescent Medicine, Hong Kong Children's Hospital, Hong Kong SAR, China.
Ho Ming LukDepartment of Clinical Genetics, Hong Kong Children's Hospital, Hong Kong SAR, China.
Rosanna Ming-Sum WongDepartment of Paediatrics and Adolescent Medicine, Hong Kong Children's Hospital, Hong Kong SAR, China.
Wanling YangDepartment of Paediatrics and Adolescent Medicine, School of Clinical Medicine, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong SAR, China.
Ivan Fai-Man LoDepartment of Clinical Genetics, Hong Kong Children's Hospital, Hong Kong SAR, China.
Cheuk-Wing FungDepartment of Paediatrics and Adolescent Medicine, Hong Kong Children's Hospital, Hong Kong SAR, China.
Joanna Yuet-Ling TungDepartment of Paediatrics and Adolescent Medicine, Hong Kong Children's Hospital, Hong Kong SAR, China.
Anthony Pak-Yin LiuDepartment of Paediatrics and Adolescent Medicine, School of Clinical Medicine, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong SAR, China; Department of Paediatrics and Adolescent Medicine, Hong Kong Children's Hospital, Hong Kong SAR, China.
Kit-San YeungDepartment of Paediatrics and Adolescent Medicine, Hong Kong Children's Hospital, Hong Kong SAR, China.
Sheila Suet-Na WongDepartment of Paediatrics and Adolescent Medicine, Hong Kong Children's Hospital, Hong Kong SAR, China.
Christopher Chun-Yu MakDepartment of Paediatrics and Adolescent Medicine, School of Clinical Medicine, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong SAR, China.
Brian Hon-Yin ChungDepartment of Paediatrics and Adolescent Medicine, School of Clinical Medicine, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong SAR, China; Hong Kong Genome Institute, Hong Kong SAR, China; Department of Paediatrics and Adolescent Medicine, Hong Kong Children's Hospital, Hong Kong SAR, China. Electronic address: bhychung@hku.hk.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundDespite significant advancements in genetic diagnosis, there are still bottlenecks in DNA-level testing. Challenges to diagnosis include the inability to identify the causal variant, and the lack of functional evidence leading to an accumulation of variants of uncertain significance (VUS). Recently, there has been growing evidence demonstrating the diagnostic value of RNA sequencing (RNA-seq).

methodsThis diagnostic study implemented RNA-seq analysis of blood (and fibroblasts, if available) in 102 patients with genetically undiagnosed diseases. An outlier analysis for gene expression and splicing was adopted through a multi-modal machine learning algorithm (i.e., Detection of RNA Outliers Pipeline-DROP).

findingsOur analysis aided the interpretation of 22/102 (21.6%) patients through both hypothesis-driven (i.e., with a prior genetic candidate; n = 10) and hypothesis-free (i.e., without a prior genetic candidate; n = 12) approaches. Not only did this workflow aid genetic diagnosis (n = 12), but it also provided additional information to known findings (n = 4) and guided the discovery of unestablished disease mechanisms (n = 6).

interpretationThis study demonstrated the clinical and scientific value of blood transcriptomics through both hypothesis-driven and hypothesis-free approaches. We have proposed an initial framework for RNA-seq implementation into the American College of Medical Genetics and Genomics and the Association for Molecular Pathology (ACMG/AMP) guidelines using PVS1 (null variants) and PP4 (phenotypic specificity). However, other considerations are required, including further clarifications for thresholds, and detailed guidance on the incorporation of different aspects of RNA-seq results (e.g., degree of nonsense-mediated decay and completeness of splicing).

fundingThis study was supported by grants from the Society for the Relief of Disabled Children, the Health and Medical Research Fund (HMRF) and Commissioned Paediatric Research at HKCH under HMRF both by the Health Bureau, The Government of the Hong Kong Special Administrative Region.

Indexed as

Gene Expression ProfilingGenetic VariationTranscriptomeComputational BiologyHumansSequence Analysis, RNAWorkflowACMG variant curationGenetic diagnosisHypothesis-drivenHypothesis-freeOutlier approachTranscriptomics

Identifiers

PMID42208150
PMCPMC13233572

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